from __future__ import annotations

import math


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int			,       UpperCamelCase     :				bool			,       UpperCamelCase     :				list[int]			,       UpperCamelCase     :				float							)   ->    int:

		if depth < 0:
				raise ValueError('Depth cannot be less than 0'							)

		if len(UpperCamelCase							) == 0:
				raise ValueError('Scores cannot be empty'							)

		if depth == height:
				return scores[node_index]

		if is_max:
				return max(
				    minimax(depth + 1			,       node_index * 2			,       UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)			,       minimax(depth + 1			,       node_index * 2 + 1			,       UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)			,       )

		return min(
		    minimax(depth + 1			,       node_index * 2			,       UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)			,       minimax(depth + 1			,       node_index * 2 + 1			,       UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)			,       )


def 	lowerCamelCase					(	)   ->    None:
		_lowerCamelCase				   =  [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
		_lowerCamelCase				   =  math.log(len(UpperCamelCase							)			,       2							)
		print('Optimal value : '			,       end=''							)
		print(minimax(0			,       0			,       UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)							)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
A						=			logging.get_logger(__name__)


def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]							)   ->    Any:
		# initialize config
		if "resnet-50" in model_name:
				_lowerCamelCase				   =  ResNetConfig.from_pretrained('microsoft/resnet-50'							)
		elif "resnet-101" in model_name:
				_lowerCamelCase				   =  ResNetConfig.from_pretrained('microsoft/resnet-101'							)
		else:
				raise ValueError('Model name should include either resnet50 or resnet101'							)

		_lowerCamelCase				   =  DetrConfig(use_timm_backbone=UpperCamelCase			,       backbone_config=UpperCamelCase							)

		# set label attributes
		_lowerCamelCase				   =  'panoptic' in model_name
		if is_panoptic:
				_lowerCamelCase				   =  2_50
		else:
				_lowerCamelCase				   =  91
				_lowerCamelCase				   =  'huggingface/label-files'
				_lowerCamelCase				   =  'coco-detection-id2label.json'
				_lowerCamelCase				   =  json.load(open(hf_hub_download(UpperCamelCase			,       UpperCamelCase			,       repo_type='dataset'							)			,       'r'							)							)
				_lowerCamelCase				   =  {int(UpperCamelCase							): v for k, v in idalabel.items()}
				_lowerCamelCase				   =  idalabel
				_lowerCamelCase				   =  {v: k for k, v in idalabel.items()}

		return config, is_panoptic


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[int]							)   ->    Dict:
		# here we list all keys to be renamed (original name on the left, our name on the right)
		_lowerCamelCase				   =  []

		# stem
		# fmt: off
		rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')							)
		rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')							)
		rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')							)
		rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')							)
		rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')							)
		# stages
		for stage_idx in range(len(config.backbone_config.depths							)							):
				for layer_idx in range(config.backbone_config.depths[stage_idx]							):
						# shortcut
						if layer_idx == 0:
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
								    )							)
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
								    )							)
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
								    )							)
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
								    )							)
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
								    )							)
						# 3 convs
						for i in range(3							):
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
								    )							)
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
								    )							)
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
								    )							)
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
								    )							)
								rename_keys.append(
								    (
								        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
								        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
								    )							)
    # fmt: on

		for i in range(config.encoder_layers							):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append(
				    (
				        F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
				        F"""encoder.layers.{i}.self_attn.out_proj.weight""",
				    )							)
				rename_keys.append(
				    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")							)
				rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight""")							)
				rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias""")							)
				rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight""")							)
				rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias""")							)
				rename_keys.append(
				    (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")							)
				rename_keys.append(
				    (F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias""")							)
				rename_keys.append(
				    (F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight""")							)
				rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias""")							)
				# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
				rename_keys.append(
				    (
				        F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
				        F"""decoder.layers.{i}.self_attn.out_proj.weight""",
				    )							)
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")							)
				rename_keys.append(
				    (
				        F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
				        F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
				    )							)
				rename_keys.append(
				    (
				        F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
				        F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
				    )							)
				rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight""")							)
				rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias""")							)
				rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight""")							)
				rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias""")							)
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")							)
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias""")							)
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")							)
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")							)
				rename_keys.append(
				    (F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight""")							)
				rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias""")							)

		# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
		rename_keys.extend(
		    [
		        ('input_proj.weight', 'input_projection.weight'),
		        ('input_proj.bias', 'input_projection.bias'),
		        ('query_embed.weight', 'query_position_embeddings.weight'),
		        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
		        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
		        ('class_embed.weight', 'class_labels_classifier.weight'),
		        ('class_embed.bias', 'class_labels_classifier.bias'),
		        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
		        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
		        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
		        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
		        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
		        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
		    ]							)

		return rename_keys


def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				Optional[int]			,       UpperCamelCase     :				Any							)   ->    Any:
		_lowerCamelCase				   =  state_dict.pop(UpperCamelCase							)
		_lowerCamelCase				   =  val


def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				List[Any]=False							)   ->    List[str]:
		_lowerCamelCase				   =  ''
		if is_panoptic:
				_lowerCamelCase				   =  'detr.'

		# first: transformer encoder
		for i in range(6							):
				# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
				_lowerCamelCase				   =  state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""							)
				_lowerCamelCase				   =  state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""							)
				# next, add query, keys and values (in that order) to the state dict
				_lowerCamelCase				   =  in_proj_weight[:2_56, :]
				_lowerCamelCase				   =  in_proj_bias[:2_56]
				_lowerCamelCase				   =  in_proj_weight[2_56:5_12, :]
				_lowerCamelCase				   =  in_proj_bias[2_56:5_12]
				_lowerCamelCase				   =  in_proj_weight[-2_56:, :]
				_lowerCamelCase				   =  in_proj_bias[-2_56:]
		# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
		for i in range(6							):
				# read in weights + bias of input projection layer of self-attention
				_lowerCamelCase				   =  state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""							)
				_lowerCamelCase				   =  state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""							)
				# next, add query, keys and values (in that order) to the state dict
				_lowerCamelCase				   =  in_proj_weight[:2_56, :]
				_lowerCamelCase				   =  in_proj_bias[:2_56]
				_lowerCamelCase				   =  in_proj_weight[2_56:5_12, :]
				_lowerCamelCase				   =  in_proj_bias[2_56:5_12]
				_lowerCamelCase				   =  in_proj_weight[-2_56:, :]
				_lowerCamelCase				   =  in_proj_bias[-2_56:]
				# read in weights + bias of input projection layer of cross-attention
				_lowerCamelCase				   =  state_dict.pop(
				    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""							)
				_lowerCamelCase				   =  state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""							)
				# next, add query, keys and values (in that order) of cross-attention to the state dict
				_lowerCamelCase				   =  in_proj_weight_cross_attn[:2_56, :]
				_lowerCamelCase				   =  in_proj_bias_cross_attn[:2_56]
				_lowerCamelCase				   =  in_proj_weight_cross_attn[2_56:5_12, :]
				_lowerCamelCase				   =  in_proj_bias_cross_attn[2_56:5_12]
				_lowerCamelCase				   =  in_proj_weight_cross_attn[-2_56:, :]
				_lowerCamelCase				   =  in_proj_bias_cross_attn[-2_56:]


def 	lowerCamelCase					(	)   ->    Tuple:
		_lowerCamelCase				   =  'http://images.cocodataset.org/val2017/000000039769.jpg'
		_lowerCamelCase				   =  Image.open(requests.get(UpperCamelCase			,       stream=UpperCamelCase							).raw							)

		return im


@torch.no_grad()
def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				Dict=None			,       UpperCamelCase     :				str=False							)   ->    Dict:
		_lowerCamelCase   ,							_lowerCamelCase				   =  get_detr_config(UpperCamelCase							)

		# load original model from torch hub
		_lowerCamelCase				   =  {
		    'detr-resnet-50': 'detr_resnet50',
		    'detr-resnet-101': 'detr_resnet101',
		}
		logger.info(F"""Converting model {model_name}..."""							)
		_lowerCamelCase				   =  torch.hub.load('facebookresearch/detr'			,       model_name_to_original_name[model_name]			,       pretrained=UpperCamelCase							).eval()
		_lowerCamelCase				   =  detr.state_dict()
		# rename keys
		for src, dest in create_rename_keys(UpperCamelCase							):
				if is_panoptic:
						_lowerCamelCase				   =  'detr.' + src
				rename_key(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)
		# query, key and value matrices need special treatment
		read_in_q_k_v(UpperCamelCase			,       is_panoptic=UpperCamelCase							)
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		_lowerCamelCase				   =  'detr.model.' if is_panoptic else 'model.'
		for key in state_dict.copy().keys():
				if is_panoptic:
						if (
						    key.startswith('detr'							)
						    and not key.startswith('class_labels_classifier'							)
						    and not key.startswith('bbox_predictor'							)
						):
								_lowerCamelCase				   =  state_dict.pop(UpperCamelCase							)
								_lowerCamelCase				   =  val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								_lowerCamelCase				   =  state_dict.pop(UpperCamelCase							)
								_lowerCamelCase				   =  val
						elif key.startswith('bbox_attention'							) or key.startswith('mask_head'							):
								continue
						else:
								_lowerCamelCase				   =  state_dict.pop(UpperCamelCase							)
								_lowerCamelCase				   =  val
				else:
						if not key.startswith('class_labels_classifier'							) and not key.startswith('bbox_predictor'							):
								_lowerCamelCase				   =  state_dict.pop(UpperCamelCase							)
								_lowerCamelCase				   =  val

    # finally, create HuggingFace model and load state dict
		_lowerCamelCase				   =  DetrForSegmentation(UpperCamelCase							) if is_panoptic else DetrForObjectDetection(UpperCamelCase							)
		model.load_state_dict(UpperCamelCase							)
		model.eval()

		# verify our conversion on an image
		_lowerCamelCase				   =  'coco_panoptic' if is_panoptic else 'coco_detection'
		_lowerCamelCase				   =  DetrImageProcessor(format=UpperCamelCase							)

		_lowerCamelCase				   =  processor(images=prepare_img()			,       return_tensors='pt'							)
		_lowerCamelCase				   =  encoding['pixel_values']

		_lowerCamelCase				   =  detr(UpperCamelCase							)
		_lowerCamelCase				   =  model(UpperCamelCase							)

		assert torch.allclose(outputs.logits			,       original_outputs['pred_logits']			,       atol=1e-3							)
		assert torch.allclose(outputs.pred_boxes			,       original_outputs['pred_boxes']			,       atol=1e-3							)
		if is_panoptic:
				assert torch.allclose(outputs.pred_masks			,       original_outputs['pred_masks']			,       atol=1e-4							)
		print('Looks ok!'							)

		if pytorch_dump_folder_path is not None:
				# Save model and image processor
				logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""							)
				Path(UpperCamelCase							).mkdir(exist_ok=UpperCamelCase							)
				model.save_pretrained(UpperCamelCase							)
				processor.save_pretrained(UpperCamelCase							)

		if push_to_hub:
				# Upload model and image processor to the hub
				logger.info('Uploading PyTorch model and image processor to the hub...'							)
				model.push_to_hub(F"""nielsr/{model_name}"""							)
				processor.push_to_hub(F"""nielsr/{model_name}"""							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()

				parser.add_argument(
				    '--model_name',
				    default='detr-resnet-50',
				    type=str,
				    choices=['detr-resnet-50', 'detr-resnet-101'],
				    help='Name of the DETR model you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
				)
				parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
				A						=			parser.parse_args()
				convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)